Debiased Machine Learning for Conformal Prediction of Counterfactual Outcomes Under Runtime Confounding

ArXi:2604.03772v1 Announce Type: cross Data-driven decision making frequently relies on predicting counterfactual outcomes. In practice, researchers commonly train counterfactual prediction models on a source dataset to inform decisions on a possibly separate target population. Conformal prediction has arisen as a popular method for producing assumption-lean prediction intervals for counterfactual outcomes that would arise under different treatment decisions in the target population of interest.